Systems and methods for image data acquisition

ABSTRACT

The present disclosure provides a system and method for image data acquisition. The method may include acquiring physiological data of a subject. The physiological data may correspond to a motion of the subject over time. The method may include obtaining a trained machine learning model configured to detect feature data represented in the physiological data. The method may include determining, based on the physiological data, an output result of the trained machine learning model that is generated based on the feature data. The method may include acquiring, based on the output result, image data of the subject using an imaging device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/870,924, filed on May 9, 2020, which claims priority to ChinesePatent Application No. 201911059466.8, filed on Nov. 1, 2019, thecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to medical system and method, and moreparticularly relates to model based controlling of medical systems andmethods.

BACKGROUND

A medical procedure directed to a subject may be adversely affected by aphysiological motion of the subject. For instance, chest and/or abdomenscanning of magnetic resonance imaging (MRI) and/or positron emissiontomography (PET) may be often affected by respiratory movement andheartbeat. For example, the cardiac and/or respiratory movement may leadto loss of MR signals, thereby affecting the quality of the image. Inorder to reduce the effects of the cardiac and/or respiratory movementon the quality of the image, a gating acquisition technique may bewidely used for image data acquisition, such as an electrocardiogramgating technique, a pulse gating technique, a respiratory gatingtechnique, etc. Using a gating acquisition technique for image dataacquisition, one or more R waves may be detected from physiological data(e.g., ECG data). However, in an imaging scan, various factors, such asa patient with heart disease, a magnetic field of an MR scanner, etc.,may cause poor accuracy of R wave detection. Thus, it is desired toprovide systems and methods for controlling the execution of a medicalprocedure to improve its accuracy and/or efficiency by reducing theimpact of a physiological motion.

SUMMARY

According to a first aspect of the present disclosure, a system isprovided. The system may include at least one storage device storingexecutable instructions, and at least one processor in communicationwith the at least one storage device. When executing the executableinstructions, the at least one processor may cause the system to performone or more of the following operations. The system may acquirephysiological data of a subject. The physiological data may correspondto a motion of the subject over time. The system may obtain a trainedmachine learning model. The system may determine, based on thephysiological data, an output result of the trained machine learningmodel. The system may acquire, based on the output result, image data ofthe subject using an imaging device.

In some embodiments, the physiological data may include at least one ofelectrocardiogram (ECG) data or respiration data.

In some embodiments, the output result may include at least one of: thefeature data represented in the physiological data; a determination asto whether a trigger condition for triggering the imaging device toacquire the image data is satisfied; or a gating weighting functiondefined by a plurality of weighting values corresponding to thephysiological data.

In some embodiments, the feature data may include position informationassociated with at least one of a peak of an R wave, a rising edge ofthe R wave, a falling edge of the R wave in the physiological data, apeak of a P wave, a rising edge of the P wave, or a falling edge of theP wave in the physiological data.

In some embodiments, to acquire, based on the output result, image dataof the subject using an imaging device, the at least one processor maybe configured to cause the system to perform the following operations.In response to determining that the trigger condition is satisfied, thesystem may generate a trigger pulse signal based on the output result.The system may also cause the imaging device to scan the subject basedat least in part on the trigger pulse signal.

In some embodiments, the trigger pulse signal may include a triggerdelay for acquiring the image data from a reference time point.

In some embodiments, to acquire, based on the output result, image dataof the subject using an imaging device, the at least one processor maybe configured to cause the system to perform the following operations.The system may acquire original image data of the subject by the imagingdevice synchronously with the acquisition of the physiological data bythe monitoring device. The system may also determine the image data fromthe original image data based on the feature data or the gatingweighting function.

In some embodiments, the physiological data may be acquired by amonitoring device based on at least one of: an echo signal generated byemitting, by the monitoring device, an electromagnetic wave to thesubject, an ECG signal, a photoelectric signal generated by emitting, bythe monitoring device, light beams to the subject, an oscillation signalgenerated when the monitoring device detects an oscillation caused by amotion of the subject, or a pressure signal generated when themonitoring device detects a pressure change caused by the motion of thesubject.

In some embodiments, the trained machine learning model may be providedby a process. The process may include obtaining a plurality of trainingsamples. The process may also include initializing parameter values of amachine learning model. The process may further include generating thetrained machine learning model by iteratively updating, based on theplurality of training samples, the parameter values of the machinelearning model.

In some embodiments, the iteratively updating, based on the plurality oftraining samples, the parameter values of the machine learning model mayinclude performing an iterative process. Each iteration of the iterativeprocess may include inputting at least one training sample of theplurality of training samples into the machine learning model. Eachiteration of the iterative process may include generating, based on theat least one training sample, an estimated output using the machinelearning model. Each iteration of the iterative process may includeobtaining an assessment result by assessing a difference between theestimated output and a reference output corresponding to the at leastone training sample. Each iteration of the iterative process may includedetermining whether a termination condition is satisfied. Based on adetermination whether the termination condition is satisfied, eachiteration of the iterative process may include updating, based on theassessment result, at least some of the parameter values of the machinelearning model in response to the determination that the terminationcondition is not satisfied; or designating the machine learning modelwith the parameter values updated in a last iteration as the deepmachine learning model in response to the determination that thetermination condition is satisfied.

In some embodiments, the obtaining an assessment result by assessing adifference between the estimated output and a reference output mayinclude determining a value of a cost function relating to thedifference between the estimated output and the reference output.

In some embodiments, the cost function may include a Softmax crossentropy loss function or a square error loss function.

In some embodiments, the plurality of training samples may include aplurality of positive samples and a plurality of negative samples. Insome embodiments, each of the plurality of positive samples may includefirst physiological data that includes feature data located within aspecific section of a time period of the first physiological data, andeach of the plurality of negative samples may include secondphysiological data that lacks the feature data located within thespecific section of a time period of the second physiological data.

In some embodiments, a length of the specific section of the time periodmay be less than a length of an acquisition window of the imaging devicefor acquiring the image data.

In some embodiments, to determine, based on the physiological data, anoutput result of a trained machine learning model, the at least oneprocessor may be configured to cause the system to perform the followingoperations. The system may perform a pretreatment operation on thephysiological data to obtain preprocessed physiological data. The systemmay also generate the output result by inputting the preprocessedphysiological data into the trained machine learning model.

In some embodiments, the pretreatment operation may include at least oneof a normalization operation, a denoising operation, a smoothingoperation, or a downsampling operation.

According to a second aspect of the present disclosure, a system isprovided. The system may include at least one storage device storingexecutable instructions, and at least one processor in communicationwith the at least one storage device. When executing the executableinstructions, the at least one processor may cause the system to performone or more of the following operations. The system may obtain aplurality of training samples. Each of the plurality of training samplesincluding physiological data of a subject. The system may generate atrained machine learning model by training, based on the plurality oftraining samples, a machine learning model. In some embodiments, thetrained machine learning model may be configured to perform at least oneof: determining feature data represented in specific physiological dataof a specific subject; determining whether a trigger condition fortriggering an imaging device to acquire image data of the specificsubject is satisfied based on the specific physiological data; ordetermining a gating weighting function defined by a plurality ofweighting values corresponding to the specific physiological data basedon the specific physiological data.

In some embodiments, to generate a trained machine learning model bytraining, based on the plurality of training samples, a machine learningmodel, the at least one processor may be configured to cause the systemto perform the following operations. The system may obtain a pluralityof training samples. The system may also initialize parameter values ofa machine learning model. The system may further generate the trainedmachine learning model by iteratively updating, based on the pluralityof training samples, the parameter values of the machine learning model.

In some embodiments, the iteratively updating, based on the plurality oftraining samples, the parameter values of the machine learning model mayinclude performing an iterative process. Each iteration of the iterativeprocess may include inputting at least one training sample of theplurality of training samples into the machine learning model. Eachiteration of the iterative process may include generating, based on theat least one training sample, an estimated output using the machinelearning model. Each iteration of the iterative process may includeobtaining an assessment result by assessing a difference between theestimated output and a reference output corresponding to the at leastone training sample. Each iteration of the iterative process may includedetermining whether a termination condition is satisfied. Based on adetermination whether the termination condition is satisfied, eachiteration of the iterative process may include updating, based on theassessment result, at least some of the parameter values of the machinelearning model in response to the determination that the terminationcondition is not satisfied; or designating the machine learning modelwith the parameter values updated in a last iteration as the deepmachine learning model in response to the determination that thetermination condition is satisfied.

According to a second aspect of the present disclosure, a method isprovided. The method may acquire physiological data of a subject, thephysiological data corresponding to a motion of the subject over time.The method may also obtain a trained machine learning model configuredto detect feature data represented in the physiological data. The methodmay further determine, based on the physiological data, an output resultof the trained machine learning model that is generated based on thefeature data. The method may further acquire, based on the outputresult, image data of the subject using an imaging device.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not scaled. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device on which the processingdevice may be implemented according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 4A is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 4B is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for image dataacquisition according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga second image based on an iterative process according to someembodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary training process of atrained machine learning model according to some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for image dataacquisition according to some embodiments of the present disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary CNN modelaccording to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary ECG accordingto some embodiments of the present disclosure;

FIG. 11 is a schematic diagram illustrating first physiological datasatisfying a trigger condition according to some embodiments of thepresent disclosure;

FIG. 12 is a schematic diagram illustrating second physiological datanot satisfying a trigger condition according to some embodiments of thepresent disclosure; and

FIGS. 13A-13C are schematic diagrams illustrating an exemplary processfor physiological data acquisition according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown but is to be accordedthe widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including” when used in this disclosure, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage devices. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices may be provided on a computer-readable medium, such asa compact disc, a digital video disc, a flash drive, a magnetic disc, orany other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in firmware, such as an erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments in the presentdisclosure. It is to be expressly understood, the operations of theflowchart may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Provided herein are systems and methods for controlling the execution ofa medical procedure to improve its accuracy and/or efficiency byreducing the impact of a physiological motion. The medical procedure maybe an imaging procedure (e.g., including scanning and/or imagereconstruction) or a treatment procedure. The medical procedure may beperformed according to a model based control mechanism. For instance,the model may include a trained machine learning model. A system mayinclude at least one storage device storing executable instructions, andat least one processor in communication with the at least one storagedevice. When executing the executable instructions, the at least oneprocessor may cause the system to obtain physiological data of a subjectacquired by a monitoring device. The physiological data may correspondto a motion of the subject over time. The at least one processor mayalso cause the system to determine an output result, e.g., feature datarepresented in the physiological data, based on a trained machinelearning model. The at least one processor may further cause the medicaldevice to perform the medical procedure including, e.g., obtaining imagedata of the subject acquired by an imaging device. In some embodiments,the at least one processor may trigger the imaging device to acquire theimage data according to the identified feature data (e.g., R waves inthe physiological data which include an ECG signal or a respiratorysignal, P waves, T waves, Q waves, S waves in the ECG signal).

In some embodiments, the trained machine learning model may be providedby training a machine learning model using a plurality of trainingsamples. For instance, the training samples may include physiologicaldata of R waves of ECG signals. In a training process, the trainedmachine learning model may not only learn characteristics (e.g., shapes,peak values, locations, change rates, etc.) of R waves from theplurality of training samples, but also identify the characteristics ofR waves based on other waves (e.g., a T wave, a P wave, etc.) associatedwith the R waves. Accordingly, systems and methods as described in thepresent disclosure may analyze the physiological data including motioninformation based on the trained machine learning model, and control theexecution of the medical procedure accordingly, thereby decreasing aprobability of incorrect trigger of the execution of the medicalprocedure, e.g., image data acquisition, using a gating acquisitiontechnique, and improving the quality of the medical procedure. Theoutput result of the trained machine learning model may also be used ina subsequent analysis of data, e.g., image data, acquired during themedical procedure so that the motion information embedded in the dataare accounted for in the subsequent analysis (e.g., imagereconstruction), thereby improving the quality of the image(s) soobtained.

FIG. 1 is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure. In someembodiments, the medical system 100 may be a single-modality system or amulti-modality system. Exemplary single-modality systems may include amagnetic resonance (MR) system, a positron emission tomography (PET)system, a single-photon emission computed tomography (SPECT) system,etc. Exemplary multi-modality systems may include a magneticresonance-positron emission tomography (MR-PET) system, a PET-CT system,etc. In some embodiments, the medical system 100 may include modulesand/or components for performing imaging and/or related analysis.

Merely by way of example, as illustrated in FIG. 1 , the medical system100 may include a medical device 110, a processing device 120, a storagedevice 130, one or more terminals 140, and a network 150. The componentsin the medical system 100 may be connected in one or more of variousways. Merely by way of example, the medical device 110 may be connectedto the processing device 120 through the network 150. As anotherexample, the medical device 110 may be connected to the processingdevice 120 directly as illustrated in FIG. 1 . As a further example, theterminal(s) 140 may be connected to another component of the medicalsystem 100 (e.g., the processing device 120) via the network 150. Asstill a further example, the terminal(s) 140 may be connected to theprocessing device 120 directly as illustrated by the dotted arrow inFIG. 1 . As still a further example, the storage device 130 may beconnected to another component of the medical system 100 (e.g., theprocessing device 120) directly as illustrated in FIG. 1 , or throughthe network 150.

The medical device 110 may be configured to acquire image data relatingto at least one part of a subject and/or perform a treatment (e.g.,radiotherapy) on the at least one part of the subject. The image datarelating to at least one part of a subject may include an image (e.g.,an image slice), projection data, or a combination thereof. In someembodiments, the image data may be a two-dimensional (2D) image data, athree-dimensional (3D) image data, a four-dimensional (4D) image data,or the like, or any combination thereof. The subject may be biologicalor non-biological. For example, the subject may include a patient, aman-made object, etc. As another example, the subject may include aspecific portion, organ, and/or tissue of the patient. For example, thesubject may include the head, the neck, the thorax, the heart, thestomach, a blood vessel, soft tissue, a tumor, nodules, or the like, orany combination thereof. In some embodiments, the medical device 110 mayinclude a single modality imaging device. For example, the medicaldevice 110 may include a positron emission tomography (PET) device, asingle-photon emission computed tomography (SPECT) device, a magneticresonance imaging (MRI) device (also referred to as an MR device, an MRscanner), a computed tomography (CT) device, or the like, or anycombination thereof. In some embodiments, the medical device 110 mayinclude a multi-modality imaging device. Exemplary multi-modalityimaging devices may include a PET-CT device, a PET-MRI device, or thelike, or a combination thereof. For example, the medical device 110 mayinclude a PET device and an MRI device. The PET device may scan asubject or a portion thereof that is located within its detection regionand generate projection data relating to the subject or the portionthereof. The following descriptions are provided with reference to animaging device as the medical device 110, unless otherwise stated. It isunderstood that this is for illustration purposes and not intended to belimiting.

The processing device 120 may process data and/or information obtainedfrom the medical device 110, the terminal(s) 140, and/or the storagedevice 130. For example, the processing device 120 may acquirephysiological data of a subject using a monitoring device. Thephysiological data may correspond to a motion of the subject over time.The processing device 120 may determine feature data represented in thephysiological data based on a trained machine learning model. Theprocessing device 120 may further cause, based on the feature data, themedical device 110 to execute a medical procedure, e.g., acquiring imagedata of the subject using an imaging device or performing a treatmentprocedure by a treatment device. As another example, the processingdevice 120 may determine whether the feature data satisfies a triggercondition. In response to determining that the feature data satisfiesthe trigger condition, the processing device 120 may generate, based onthe feature data, a trigger pulse signal including a trigger delay foracquiring the image data from the time when the trigger pulse signalgenerates. The processing device 120 may cause the imaging device toscan the subject based at least in part on the trigger pulse signal. Asstill another example, the processing device 120 may acquire originalimage data of the subject by the imaging device (e.g., the medicaldevice 110) synchronously with the acquisition of the physiological databy the monitoring device. The processing device 120 may determine theimage data from the original image data based on the feature data.

In some embodiments, the processing device 120 may determine the trainedmachine learning model by training a machine learning model using aplurality of training samples obtained from a sample set. The trainedmachine learning model used in the present disclosure (e.g., the trainedmachine learning model) may be updated from time to time, e.g.,periodically or not, based on a sample set that is at least partiallydifferent from the original sample set from which the original trainedmachine learning model is determined. For instance, the trained machinelearning model (e.g., the trained machine learning model) may be updatedbased on a sample set including new samples that are not in the originalsample set. In some embodiments, the determination and/or updating ofthe trained machine learning model (e.g., the trained machine learningmodel) may be performed on a processing device, while the application ofthe trained machine learning model may be performed on a differentprocessing device. For example, the determination and/or updating of thetrained machine learning model may be performed by the modules of theprocessing device as shown in FIG. 4B. The application of the trainedmachine learning model may be performed by the modules of the processingdevice as shown in FIG. 4A. In some embodiments, the determinationand/or updating of the trained machine learning model (e.g., the trainedmachine learning model) may be performed on a processing device of asystem different than the medical system 100 or a server different thana server including the processing device 120 on which the application ofthe trained machine learning model is performed. For instance, thedetermination and/or updating of the trained machine learning model(e.g., the trained machine learning model) may be performed on a firstsystem of a vendor who provides and/or maintains such a machine learningmodel and/or has access to training samples used to determine and/orupdate the trained machine learning model, while image generation basedon the provided machine learning model may be performed on a secondsystem of a client of the vendor. In some embodiments, the determinationand/or updating of the trained machine learning model (e.g., the trainedmachine learning model) may be performed online in response to a requestfor image generation. In some embodiments, the determination and/orupdating of the trained machine learning model may be performed offline.

In some embodiments, the processing device 120 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing device120 may be local or remote. For example, the processing device 120 mayaccess information and/or data stored in the medical device 110, theterminal(s) 140, and/or the storage device 130 via the network 150. Asanother example, the processing device 120 may be directly connected tothe medical device 110, the terminal(s) 140 and/or the storage device130 to access stored information and/or data. In some embodiments, theprocessing device 120 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the terminal(s) 140 and/or the processing device 120. Thedata may include image data acquired by the processing device 120,algorithms and/or models for processing the image data, etc. Forexample, the storage device 130 may store image data (e.g., PET images,PET projection data, etc.) acquired by the medical device 110. Asanother example, the storage device 130 may store one or more algorithmsfor processing the image data, a trained machine learning model, etc. Insome embodiments, the storage device 130 may store data and/orinstructions that the processing device 120 may execute or use toperform exemplary methods/systems described in the present disclosure.In some embodiments, the storage device 130 may include a mass storage,removable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memories may include a random access memory(RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double daterate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. ExemplaryROM may include a mask ROM (MROM), a programmable ROM (PROM), anerasable programmable ROM (EPROM), an electrically erasable programmableROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile diskROM, etc. In some embodiments, the storage device 130 may be implementedon a cloud platform. Merely by way of example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in themedical system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the medical system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be directlyconnected to or communicate with one or more other components in themedical system 100 (e.g., the processing device 120, the terminal(s)140, etc.). In some embodiments, the storage device 130 may be part ofthe processing device 120.

The terminal(s) 140 may include a mobile device 141, a tablet computer142, a laptop computer 143, or the like, or any combination thereof. Insome embodiments, the mobile device 141 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 140 may be part of the processing device 120.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the medical system 100. In someembodiments, one or more components of the medical device 110 (e.g., anMRI device, a PET device, etc.), the terminal(s) 140, the processingdevice 120, the storage device 130, etc., may communicate informationand/or data with one or more other components of the medical system 100via the network 150. For example, the processing device 120 may obtaindata from the medical device 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150. The network 150 may be and/orinclude a public network (e.g., the Internet), a private network (e.g.,a local area network (LAN), a wide area network (WAN)), etc.), a wirednetwork (e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 150 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network150 may include one or more network access points. For example, thenetwork 150 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the medical system 100 may be connected to thenetwork 150 to exchange data and/or information.

In some embodiments, the medical system 100 may include a monitoringdevice (not shown) configured to acquire physiological signals (i.e.,physiological data). Exemplary physiological signals may include anelectrocardiogram (ECG) signal, an electromyogram (EMG) signal, anelectroencephalogram (EEG) signal, a respiratory signal, a pulse signal,or the like, or a combination thereof.

In some embodiment, the monitoring device may be connected to a subject(e.g., patient) via electrodes. The electrodes may acquire thephysiological signal of the subject in parallel with the medical device110. In some embodiments, the electrodes may include anelectrocardiograph electrode, a respiratory impedance electrode, amulti-electrode, or the like, or a combination thereof. For example, theelectrodes may include at least one electrocardiograph electrodecollecting the ECG signal of the subject. As another example, theelectrodes may include at least one respiratory impedance electrodecollecting the respiratory signal of the subject. In some embodiments,the electrodes may include at least one multi-electrode. Themulti-electrode may collect the electrocardiogram ECG signal, theelectromyography (EMG) signal, the electroencephalogram (EEG) signal,the respiratory signal, the pulse signal, or the like, or a combinationthereof.

In some embodiments, the monitoring device may acquire the physiologicalsignal using a thermistor sensor, an infrared sensor, a photoelectricsensor, a pressure sensor, or the like, or a combination thereof. Insome embodiments, the monitoring device may be integrated into themedical device 110.

It should be noted that the above description of the medical system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the medical system 100 may be varied or changedaccording to specific implementation scenarios.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 2 , the computing device 200may include a processor 210, a storage 220, an input/output (I/O) 230,and a communication port 240.

The processor 210 may execute computer instructions (program codes) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may processdata obtained from the medical device 110, the terminal(s) 140, thestorage device 130, and/or any other component of the medical system100. Specifically, the processor 210 may process one or more measureddata sets obtained from the medical device 110. For example, theprocessor 210 may generate an image based on the data set(s). In someembodiments, the generated image may be stored in the storage device130, the storage 220, etc. In some embodiments, the generated image maybe displayed on a display device by the I/O 230. In some embodiments,the processor 210 may perform instructions obtained from the terminal(s)140. In some embodiments, the processor 210 may include one or morehardware processors, such as a microcontroller, a microprocessor, areduced instruction set computer (RISC), an application-specificintegrated circuit (ASIC), an application-specific instruction-setprocessor (ASIP), a central processing unit (CPU), a graphics processingunit (GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field-programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the medicaldevice 110, the terminal(s) 140, the storage device 130, or any othercomponent of the medical system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and themedical device 110, the term inal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,etc.), or the like, or any combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. As illustrated in FIG. 3 , themobile device 300 may include a communication platform 310, a display320, a graphics processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS, Android, Windows Phone, etc.) and one or more applications380 may be loaded into the memory 360 from the storage 390 in order tobe executed by the CPU 340. The applications 380 may include a browseror any other suitable mobile apps for receiving and renderinginformation relating to image data acquisition or other information fromthe processing device 120. User interactions with the information streammay be achieved via the I/O 350 and provided to the processing device120 and/or other components of the medical system 100 via the network150.

To implement various modules, units, and functionalities described inthe present disclosure, computer hardware platforms may be used as thehardware platform(s) for one or more of the elements described herein.The hardware elements, operating systems and programming languages ofsuch computers are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith to adapt thosetechnologies for image data acquisition as described herein. A computerwith user interface elements may be used to implement a personalcomputer (PC) or another type of work station or terminal device,although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4A is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 200 (e.g., the processor 210) illustrated in FIG. 2 or a CPU 340as illustrated in FIG. 3 . As illustrated in FIG. 4A, the processingdevice 120 may include an acquisition module 410, a determination module420, a control module 430, and a storage module 440. Each of the modulesdescribed above may be a hardware circuit that is designed to performcertain actions, e.g., according to a set of instructions stored in oneor more storage media, and/or any combination of the hardware circuitand the one or more storage media.

The acquisition module 410 may be configured to acquire physiologicaldata of a subject. The physiological data may correspond to a motion ofthe subject (e.g., the heart, the abdomen, etc.) over time. In someembodiments, the acquisition module 410 may acquire the physiologicaldata from a storage device, for example, the storage device 130, or anyother storage (not shown). In some embodiments, the acquisition module410 may acquire the physiological data of the subject using a monitoringdevice. The monitoring device may acquire the physiological data basedon an echo signal, an ECG signal, a photoelectric signal, an oscillationsignal, a pressure signal, or the like, or a combination thereof. Insome embodiments, the acquisition module 410 may acquire thephysiological data of the subject using an imaging device (e.g., an MRIdevice).

The determination module 420 may be configured to determine an outputresult of a trained machine learning model based on the physiologicaldata. In some embodiments, the determination module 420 may retrieve thetrained machine learning model from the storage device 130, theterminal(s) 140, or any other storage device. In some embodiments, thetrained machine learning model may be configured to detect feature datafrom the physiological data. In some embodiments, the trained machinelearning model may be configured to determine whether a triggercondition for triggering the imaging device to acquire the image data issatisfied. In some embodiments, the trained machine learning model maybe configured to provide a mapping relationship between thephysiological data and a gating weighting function based on the detectedfeature data.

The output result may include the feature data represented in thephysiological data, a determination as to whether the trigger conditionis satisfied, a determination as to whether the physiological dataincludes the feature data, the gating weighting function correspondingto the physiological data, or the like, or a combination thereof. Insome embodiments, the determination module 420 may input thephysiological data into the trained machine learning model. The trainedmachine learning model may generate the output result using thephysiological data. In some embodiments, the determination module 420may perform a pretreatment operation on the physiological data to obtainpreprocessed physiological data and input the preprocessed physiologicaldata into the trained machine learning model. The trained machinelearning model may generate the output result using the preprocessedphysiological data. The pretreatment operation may include anormalization operation, a denoising operation, a smoothing operation,an downsampling operation, or the like, or a combination thereof.

The control module 430 may be configured to control a medical device toexecute a medical procedure, for example, acquire image data of thesubject. In some embodiments, if the output result indicates that thetrigger condition is satisfied, the control module 430 may generate atrigger pulse signal configured to trigger the medical device (e.g., themedical device 110) to acquire the image data according to anacquisition window and a trigger delay. In some embodiments, the controlmodule 430 may determine whether the trigger condition is satisfiedbased on the feature data identified by the trained machine learningmodel. If the feature data satisfies the trigger condition, the triggerpulse signal may be generated to cause the imaging device to scan thesubject.

In some embodiments, the control module 430 may cause the imaging deviceto scan the subject based at least in part on the trigger pulse signal.For example, if one single trigger pulse signal is generated, thecontrol module 430 may cause the imaging device to scan the subjectbased on the one single trigger pulse signal. As another example, thecontrol module 430 may determine whether a specific count (or number) ofmultiple trigger pulse signals are generated. In response to determiningthat the specific count (or number) of consecutive trigger pulse signalsare generated, the control module 430 may cause the imaging device(e.g., an MRI device) to scan the subject after a trigger delay from thetime the last trigger pulse signal generates.

In some embodiments, the control module 430 may control the image deviceto acquire original image data of the subject synchronously with theacquisition of the physiological data by the monitoring device. Thecontrol module 430 may determine the image data from the original imagedata based on the output result of the trained machine learning model.For example, the control module 430 may determine the gating weightingfunction based on the feature data identified from the physiologicaldata or obtain the gating weighting function outputted by the trainedmachine learning model. The control module 430 may extract the imagedata from the original data based on the gating weighting function. Forexample, the control module 430 may extract the image data from theoriginal image data by multiplying the gating weighting function withthe original image data.

The storage module 440 may be configured to store data and/orinstructions associated with the medical system 100. For example, thestorage module 440 may store data of the physiological data of thesubject, the trained machine learning model, the feature data, theoutput result, the trigger pulse signal, etc. In some embodiments, thestorage module 440 may be the same as the storage device 130 and/or thestorage module 470 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the acquisition module 410 and the control module 430 maybe integrated into a single module. As another example, some othercomponents/modules may be added into the processing device 120.

FIG. 4B is a block diagram illustrating another exemplary processingdevice according to some embodiments of the present disclosure. In someembodiments, processing device 120 may be implemented on a computingdevice 200 (e.g., the processor 210) illustrated in FIG. 2 or a CPU 340as illustrated in FIG. 3 . As illustrated in FIG. 4B, the processingdevice 120 may include an obtaining module 450, a training module 460,and a storage module 470. Each of the modules described above may be ahardware circuit that is designed to perform certain actions, e.g.,according to a set of instructions stored in one or more storage media,and/or any combination of the hardware circuit and the one or morestorage media.

The obtaining module 450 may be configured to obtain data regardingmodel training, for example, a plurality of training samples. Each ofthe plurality of training samples may include physiological data of asample subject. In some embodiments, the plurality of training samplesmay have the same length of time periods. For example, the length of thetime period may be equal to 800 milliseconds.

In some embodiments, each of the plurality of training samples mayinclude annotated physiological data corresponding to the physiologicaldata. The physiological data corresponding to each of the trainingsamples may be annotated by identifying the feature data (e.g., the Rwave) from the physiological data. The identification of the featuredata may include locating and/or marking the feature data from thephysiological data.

In some embodiments, each of the plurality of training samples mayinclude a label corresponding to the physiological data. Thephysiological data may be used as an input in the training process of amachine learning model, and the label corresponding to the physiologicaldata may be used as a reference output corresponding to thephysiological data in the training process of the machine learningmodel.

In some embodiments, each of the plurality of training samples mayinclude the physiological data and a gating weighting function (or agating curve) corresponding to the physiological data. The physiologicaldata may be used as input in the training process of a machine learningmodel, and the gating weighting function corresponding to thephysiological data may be used as a reference output corresponding tothe physiological data in the training process of the machine learningmodel.

In some embodiments, the plurality of training samples may include aplurality of positive samples and a plurality of negative samples. Eachof the plurality of positive samples may include first physiologicaldata that includes the feature data located at in a specific section ofa time period of the first physiological data. Each of the plurality ofnegative samples may include second physiological data that lacks thefeature data located at the specific section of a time period of thesecond physiological data. In some embodiments, in the plurality oftraining samples, a count (or number) of the negative samples may exceedthe count (or number) of the positive sample, such as 2-3 times thecount of the positive samples. In some embodiments, the obtaining module450 may perform a pretreatment operation on each of at least a portionof the plurality of training samples. The pretreatment operation mayinclude at least one of a normalization operation, a denoisingoperation, a smoothing operation, or an downsampling operation.

The training module 460 may be configured to generate a trained machinelearning model by training a machine learning model using the pluralityof training samples in a training process. In some embodiments, thetraining module 460 may construct the trained machine learning modelbased on a deep learning model (e.g., a convolutional neural network(CNN) model, a deep belief nets (DBN) machine learning model, a stackedauto-encoder network), a recurrent neural network (RNN) model, a longshort term memory (LSTM) network model, a fully convolutional neuralnetwork (FCN) model, a generative adversarial network (GAN) model, aback propagation (BP) machine learning model, a radial basis function(RBF) machine learning model, an Elman machine learning model, or thelike, or any combination thereof. The training module 460 may train themachine learning model based on the plurality of training samples usinga training algorithm. In some embodiments, the training module 460 mayperform a plurality of iterations to iteratively update one or moreparameter values of the machine learning model to obtain the trainedmachine learning model. Before the plurality of iterations, the trainingmodule 460 may initialize the parameter values of the machine learningmodel.

The storage module 470 may be configured to store data and/orinstructions associated with the medical system 100. For example, thestorage module 470 may store data of the plurality of training samples(e.g., the training samples), one or more machine learning models, thetrained machine learning model, etc. In some embodiments, the storagemodule 470 may be the same as the storage device 130 and/or the storagemodule 440 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the obtaining module 450 and the storage module 470 may beintegrated into a single module. As another example, some othercomponents/modules may be added into the processing device 120.

FIG. 5 is a flowchart illustrating an exemplary process for image dataacquisition according to some embodiments of the present disclosure. Insome embodiments, process 500 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210 and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the processor 210and/or the CPU 340 may be configured to perform the process 500. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 500 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order of theoperations of the process 500 illustrated in FIG. 5 and described belowis not intended to be limiting.

In 502, the processing device 120 (e.g., the acquisition module 410) mayacquire physiological data of a subject.

The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of the patient. For example, the subject may include the head,the neck, the thorax, the heart, the abdomen, the stomach, the limbs,the pelvic, a blood vessel, soft tissue, a tumor, nodules, or the like,or any combination thereof.

The physiological data may include electrocardiogram (ECG) data,electromyogram (EMG) data, an electroencephalogram (EEG) data,respiration data, pulse data, or the like, or a combination thereof. Thephysiological data may correspond to a motion of the subject (e.g., theheart, the abdomen, etc.) over time. In other words, the physiologicaldata may contain motion or movement information of the subject. Themovement may include incompressible organ deformation (e.g., movement ofthe liver associated with breathing), compressible organ deformation(e.g., movement of the heart associated with its beating), etc. Themovement information may include the movement rate, the movementamplitude or displacement, the movement phase of the subject, etc. Forexample, the ECG data may represent changes of heart rate or heartbeatamplitude over time.

In some embodiments, the movement phase of the subject may include aresting (or relaxation) phase with a low movement rate or amplitude anda tension phase with a high movement rate or amplitude. For example, theresting (or relaxation) phase associated with the heart may correspondto a diastolic period of the motion of the heart. The tension phaseassociated with the heart may correspond to a systolic period of themotion of the heart. As another example, the resting (or relaxation)phase associated with the respiratory motion may correspond to anexpiratory period. The tension phase associated with the respiratorymotion may correspond to an inspiratory phase. According to FIG. 9 , theheart performs a periodic movement, including an alternating sequence ofa tension phase corresponding to a time period t1 from the peak of an Rwave to the end of a T wave, and a resting (or relaxation) phasecorresponding to a time period t2 from the end of the T wave to the peakof another R wave generated in the next cycle. More descriptionsregarding the ECG data may be found in FIG. 10 and the descriptionsthereof.

In some embodiments, the processing device 120 may obtain thephysiological data from a storage device, for example, the storagedevice 130, or any other storage (not shown). For example, a monitoringdevice may acquire the physiological data of the subject during a timeperiod (e.g., 1 second, 10 seconds, 30 seconds, 1 minute, 3 minutes, 30minutes, etc.) and store the acquired physiological data in the storagedevice. The processing device 120 may obtain the physiological data froma storage device. In some embodiments, the processing device 120 mayobtain the physiological data from the monitoring device. For example,the monitoring device may acquire the physiological data of the subjectduring a time period (e.g., 1 second, 800 milliseconds, 50 milliseconds,1 millisecond, etc.) and transmit the acquired physiological data to theprocessing device 120. More descriptions for physiological dataacquisition may be found elsewhere in the present disclosure (e.g., FIG.8 , and the descriptions thereof).

The monitoring device may be configured to acquire the physiologicaldata of the subject. In some embodiments, the monitoring device mayacquire the physiological data based on an echo signal, an ECG signal, aphotoelectric signal, an oscillation signal, a pressure signal, or thelike, or a combination thereof. In some embodiments, the monitoringdevice acquiring the physiological data based on the echo signal mayalso be referred to as a non-contact detector based on an antenna. Thenon-contact detector may emit an electromagnetic wave to the subject andthe subject may generate the echo signal after receives theelectromagnetic wave. The non-contact detector may acquire the echosignal and extract the physiological data (e.g., the ECG data or therespiration data) from the echo signal. The non-contact detector mayprovide beneficial effects including, such as a non-contact acquisitionof the physiological data.

In some embodiments, the monitoring device acquiring the physiologicaldata based on the ECG signal may include a chest band electrodedetector, an electrocardiogram detector, or the like. For example, themonitoring device acquiring the physiological data based on the ECGsignal may detect multiple-lead ECG signals using electrodes. Thephysiological data acquired by the monitoring device based on the ECGsignal may be accurate.

In some embodiments, the monitoring device acquiring the physiologicaldata based on the photoelectric signal may include a monitoring devicebased on photoelectric transmission, a monitoring device based onphotoelectric reflection, or the like. The monitoring device acquiringthe physiological data based on the photoelectric signal may emit one ormore light beams to the subject and detect the photoelectric signalgenerated based on a transmitted light or a reflected light after thesubject receives the one or more light beams. The monitoring device mayconvert the photoelectric signal into the physiological data. In someembodiments, the monitoring device may include a sensor (e.g., aphotoelectric sensor) configured to generate and emit a light beam anddetect the transmitted light or the reflected light. Since blood mayabsorb light of a particular wavelength, a large amount of the light ofthe particular wavelength may be absorbed. Therefore, the physiologicaldata (e.g., the ECG data) may be detected by the monitoring device basedon the photoelectric signal reflected or transmitted by the subject. Itmay be convenient to acquire the physiological data using the monitoringdevice based on the photoelectric signal, and the monitoring devicebased on the photoelectric signal may be easy to operate.

In some embodiments, the monitoring device acquiring the physiologicaldata based on the oscillation signal may generate the oscillation signalafter the monitoring device detects an oscillation caused by the motionof the subject (e.g., an oscillation caused by heartbeat). Themonitoring device acquiring the physiological data based on theoscillation signal may include a sensor (e.g., a pressure sensor) thatis configured to detect the oscillation. The monitoring device based onthe oscillation signal may acquire the physiological data (e.g., the ECGdata) by processing and converting the oscillation signal. Themonitoring device acquiring the physiological data based on theoscillation signal may be easy to operate.

In some embodiments, the monitoring device acquiring the physiologicaldata based on the pressure signal may generate the pressure signal afterthe monitoring device detects a pressure change caused by the motion ofthe subject. The monitoring device acquiring the physiological databased on the pressure signal may include a sensor (e.g., a pulse sensor,a pressure sensor) that is configured to detect the pressure change. Themonitoring device based on the pressure signal may acquire thephysiological data (e.g., the respiration data, the pulse data) byprocessing and converting the pressure signal. In some embodiments, themonitoring device based on the pressure signal may include an abdominalband electrode detector. The abdominal band electrode detector may use apressure sensor to obtain the pressure change on an abdominal bandcaused by respiration to detect a respiratory movement (e.g., therespiratory data).

In some embodiments, the physiological data of the subject may beacquired by an imaging device. For example, an MRI device may acquire MRsignal changes caused by the respiratory movement or the heartbeatthrough a magnetic resonance navigation sequence (e.g., a navigationecho T2W1 sequence). The processing device 120 may determine thephysiological data by analyzing a motion parameter of the subject (e.g.,the position of the center of mass) characterized in the change in theMR signal.

In 504, the processing device 120 (e.g., the determination module 420)may determine, based on the physiological data, an output result of atrained machine learning model.

In some embodiments, the processing device 120 may retrieve the trainedmachine learning model from the storage device 130, the terminals(s)140, or any other storage device. For example, the trained machinelearning model may be determined by training a machine learning modeloffline based on a plurality of training samples using the processingdevice 120 or a processing device other than the processing device 120.The trained machine learning model may be stored in the storage device130, the terminals(s) 140, or any other storage device. The processingdevice 120 may retrieve the trained machine learning model from thestorage device 130, the term inals(s) 140, or any other storage devicein response to receipt of a request for image data acquisition. Moredescriptions regarding the plurality of training samples may be found inFIG. 6 . The training process of the trained machine learning model maybe performed according to process 600 and process 700.

The trained machine learning model may be configured to detect featuredata from the physiological data. As used herein, the detection of thefeature data may include determining whether the physiological dataincludes the feature data and/or identifying (e.g., locate, and/or mark)the feature data from the physiological data (or the preprocessedphysiological data). In some embodiments, the trained machine learningmodel may be further configured to determine whether a trigger conditionfor triggering the imaging device to acquire the image data is satisfiedbased on the detected feature data. In some embodiments, the trainedmachine learning model may be configured to provide a mappingrelationship between the physiological data and a gating weightingfunction based on the detected feature data.

The output result may include the feature data represented in thephysiological data, a determination as to whether the trigger conditionis satisfied, a determination as to the physiological data includes thefeature data, the gating weighting function corresponding to thephysiological data, etc.

The feature data may be used to identify a resting (or relaxation) phaseand/or a tension phase of the motion of the subject. The feature datamay include one or more feature points (e.g., a peak and/or a valley inthe physiological data), one or more feature segments (e.g., at least aportion of a wave, e.g., an R wave, a T wave, a P wave, a Q wave, a Swave), etc. For example, the feature data may include positioninformation associated with a peak of an R wave, a rising edge of the Rwave, a falling edge of the R wave, the R wave, etc., in the ECG data(e.g., an electrocardiogram) and/or in the respiration data. As anotherexample, the feature data may include position information associatedwith a peak of a T wave, a rising edge of the T wave, a falling edge ofthe T wave, the T wave, etc., in the ECG data (e.g., anelectrocardiogram). As still another example, the feature data mayinclude at least a portion of each of one or more resting phases (e.g.,a diastolic period) and/or at least a portion of each of one or moretension phases (e.g., a systolic period) of the motion of the subject.As still another example, the feature data may include positioninformation associated with a peak of a P wave, a rising edge of the Pwave, a falling edge of the P wave, the P wave, etc., in the ECG data(e.g., an electrocardiogram). As used herein, the position informationassociated with a feature point or feature segment in the physiologicaldata refers to time information when the feature point or featuresegment presented in the physiological data.

In some embodiments, the output result may include the time informationof the feature data. In some embodiments, the output result may includeat least a portion of the physiological data with identified featuredata. For example, the trained machine learning model may locate and/ormark the feature data (e.g., the position information associated with atleast one of the peak of the R wave, the rising edge of the R wave, orthe falling edge of the R wave in the ECG data) in the physiologicaldata. The trained machine learning model may output the physiologicaldata with the marked feature data or output the feature data (e.g., theposition information associated with at least one of the peak of the Rwave, the rising edge of the R wave, or the falling edge of the R wavein the ECG data). In some embodiments, the feature data may be presentedin the form of text, a curve, an image, etc. In some embodiments, thefeature data may be marked using a bounding box. The bounding box mayenclose feature data (e.g., the R wave) in the training sample. Thebounding box may have any shape and/or size. For example, the boundingbox may have the shape of a square, a rectangle, a triangle, a polygon,a circle, an ellipse, an irregular shape, or the like. In someembodiments, the feature data may be marked using an arrow, a highlight,a line type or color, or the like, or a combination thereof.

In some embodiments, the output result may indicate whether thephysiological data includes the feature data (e.g., at least a portionof an R wave). For example, if the physiological data includes thefeature data (e.g., at least a portion of an R wave), the trainedmachine learning model may generate the output result indicating a truthvalue (e.g., 1). If the physiological data lacks the feature data (e.g.,at least a portion of an R wave), the trained machine learning model maygenerate the output result indicating a false value (e.g., 0).

In some embodiments, the output result may indicate whether the triggercondition is satisfied. For example, if the trigger condition issatisfied, the trained machine learning model may generate the outputresult indicating a truth value (e.g., 1). If the trigger condition isunsatisfied, the trained machine learning model may generate the outputresult indicating a false value (e.g., 0). The trigger condition mayindicate that an imaging device (e.g., an MR scanner) may be triggeredto scan the subject after a trigger delay from the time when the featuredata (e.g., the R wave) is detected in the physiological data.

The gating weighting function may be used to determine a portion oforiginal image data of the subject acquired by an imaging devicesynchronously with the acquisition of the physiological data by themonitoring device. The gating weighting function may include a pluralityof weighting values corresponding to the physiological data. Each of theplurality of weighting values may correspond to a portion of thephysiological data acquired at a time or period. The weighting valuesmay be in a range from 0 to 1. For example, if the physiological motionof the subject is significant during the tension phase (e.g., timeperiod t1 as shown in FIG. 10 ), weighting values corresponding to thetension phase may be relatively small, such as approximate to or equalto 0; if the physiological motion of the subject is mild during theresting phase (e.g., time period t1 as shown in FIG. 10 ), the weightingvalues corresponding to the resting phase may be relatively large, suchas approximate to or equal to 1.

In some embodiments, the processing device 120 may input thephysiological data into the trained machine learning model. The trainedmachine learning model may generate the output result using thephysiological data. In some embodiments, since the physiological data ofthe subject may include abnormal data and/or noise signals, theprocessing device 120 may perform a pretreatment operation on thephysiological data to obtain preprocessed physiological data. Theprocessing device 120 may input the preprocessed physiological data intothe trained machine learning model. The trained machine learning modelmay generate the output result using the preprocessed physiologicaldata.

In some embodiments, the pretreatment operation may include anormalization operation, a denoising operation, a smoothing operation,an downsampling operation, or the like, or a combination thereof.

In some embodiments, the normalization operation may be used tonormalize amplitudes of the physiological data using a normalizationalgorithm, such as the z-score correction algorithm. For example, theprocessing device 120 may perform the normalization operation on thephysiological data using the z-score correction algorithm according tothe following Equation (1):

Data_(N)=(Data_(O)−Data_(M))/Se,  (1)

where Data_(N) refers to normalized physiological data (i.e., thepreprocessed physiological data), Data_(O) refers to the physiologicaldata obtained in 502, Data_(M) refers to a mean of specificphysiological data of the subject acquired in a certain time period, andSe refers to a square error of the specific physiological data acquiredin the certain time period. The specific physiological data acquired inthe certain time period may refer to physiological data acquired in anytime period different from the physiological data obtained in 502, forexample, physiological data at the beginning of an imaging scanning,physiological data during the imaging scanning, or the like. The mean ofthe specific physiological data may refer to an average value ofamplitudes of the specific physiological data. The square error of thespecific physiological data may refer to a square error of theamplitudes of the specific physiological data. The normalizedphysiological data may fit a standard normal distribution. That is, themean of the normalized physiological data may be 0, and the standarddeviation of the normalized physiological data may be 1. Thenormalization operation on the physiological data may reduce effects ofamplitude differences of physiological data of different subjects, andsuppress noise interference.

In some embodiments, the processing device 120 may perform the smoothingoperation on the physiological data using a smoothing algorithm, e.g., a2n+1 points simplex moving average filtering algorithm, a weightingmoving average filtering algorithm, a smoothing filtering algorithmusing a smooth function, a one dimensional (1D) median filteringalgorithm, or the like, or any combination thereof.

In some embodiments, the processing device 120 may perform thedownsampling operation on the physiological data using an anti-aliasingfilter, which may retain basic features of the physiological data andreducing an amount of physiological data to be processed.

In some embodiments, the processing device 120 may perform the denoisingoperation on the physiological data using a denoising algorithm, such asusing the PauTa criterion (i.e., the 3σ criterion), the Chauvenetcriterion, a first order difference algorithm, etc. The pretreatmentoperations may reduce effects of the amplitude differences ofphysiological data of different subjects, the noises in thephysiological data, the abnormal values in the physiological data, etc.,on the identification of the feature data. In addition, the pretreatmentoperation (e.g., the downsampling operation) may decrease data quantityto be processed using the trained machine learning model, therebyreducing the time of identifying the feature data.

In 506, the processing device 120 (e.g., the control module 430) mayacquire image data of the subject using an imaging device based on thefeature data.

The imaging device may be configured to acquire image data relating toat least one part of a subject. More descriptions regarding the imagingdevice may be found in FIG. 1 and the descriptions thereof. In someembodiments, if the output result indicates that the trigger conditionis satisfied, the processing device 120 may generate a trigger pulsesignal configured to trigger the image data to acquire the image dataaccording to an acquisition window and a trigger delay. The length ofthe acquisition window and the trigger delay may be set by a user oraccording to a default setting of the medical system 100. Theacquisition of the image data based on the physiological data may alsobe referred to as a gating acquisition technique (such as prospectiveacquisition).

In some embodiments, the processing device 120 may determine whether thetrigger condition is satisfied based on the feature data identified bythe trained machine learning model. If the feature data (e.g., theposition information associated with at least one of the peak of the Rwave, the rising edge of the R wave, or the falling edge of the R wavein the ECG data) satisfies the trigger condition, the trigger pulsesignal may be generated to cause the imaging device to scan the subject.More descriptions regarding image data acquisition based on the triggerpulse signal may be found in FIG. 8 and the descriptions thereof.

In some embodiments, the processing device 120 may obtain original imagedata of the subject acquired by the imaging device synchronously withthe acquisition of the physiological data by the monitoring device. Theprocessing device 120 (e.g., the control module 430) may determine theimage data from the original image data based on the output result ofthe trained machine learning model. For example, the processing device120 may determine the gating weighting function (also referred to as agating curve) based on the feature data identified from thephysiological data or obtain the gating weighting function outputted bythe trained machine learning model. The processing device 120 mayextract the image data from the original data based on the gatingweighting function. For example, the processing device 120 may extractthe image data from the original image data by multiplying the gatingweighting function with the original image data. The image data mayinclude a portion of the original image data acquired by the imagingdevice (e.g., the medical device 110 in FIG. 1 ) at a time when theweighting values are non-zero. Each of the plurality of weighting valuesmay correspond to a time or period. Each of the plurality of weightingvalues may be multiplied with a portion of the original image dataacquired by at a corresponding time or period. Image data acquired at atime or period (e.g., t2 time period as shown in FIG. 10 ) correspondingto weighting values non-zero may be used for reconstructing an image,while image data acquired at a time or period corresponding to weightingvalues 0 (e.g., e.g., t1 time period as shown in FIG. 10 ) may beremoved away.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, one or more operations may be omitted and/or one or moreadditional operations may be added. For example, operation 502 andoperation 504 may be combined into a single operation. As anotherexample, one or more other optional operations (e.g., a storingoperation) may be added elsewhere in the process 500. In the storingoperation, the processing device 120 may store information and/or data(e.g., the physiological data, the feature data, the trained machinelearning model, etc.) associated with the medical system 100 in astorage device (e.g., the storage device 130) disclosed elsewhere in thepresent disclosure.

FIG. 6 is a flowchart illustrating an exemplary process for training amachine learning model according to some embodiments of the presentdisclosure. In some embodiments, process 600 may be implemented as a setof instructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210 and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the processor 210and/or the CPU 340 may be configured to perform the process 600. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 600 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order of theoperations of process 600 illustrated in FIG. 6 and described below isnot intended to be limiting. In some embodiments, the training processof the trained machine learning model as described in connection withoperation 504 in FIG. 5 may be performed according to the process 600.

In 602, the processing device 120 (e.g., the obtaining module 450) mayobtain a plurality of training samples. Each of the plurality oftraining samples may include physiological data of a sample subject. Thefollowing descriptions of FIG. 6 are provided with reference tophysiological data relating to the cardiac motion unless otherwisestated. It is understood that this is for illustration purposes and notintended to be limiting.

The physiological data may include electrocardiogram (ECG) data,electromyogram (EMG) data, an electroencephalogram (EEG) data,respiration data, pulse data, or the like, or a combination thereof. Thesample subjects corresponding to the plurality of training samples maybe the same or different. The physiological data may be acquired by amonitoring device during a time period. The time period may be 1.5seconds, 1 second, 800 milliseconds, 500 milliseconds, etc. Moredescriptions for the physiological data may be found elsewhere in thepresent disclosure.

In some embodiments, in order to exclude the influence of lengths oftime periods of the training samples on the training of the machinelearning model, the plurality of training samples may have the samelength of time periods. In some embodiments, if the physiological dataincludes the ECG data, the length of the time period of thephysiological data may be less than or equal to a cardiac cycle. Thecardiac cycle of a person may be in a range from 400 milliseconds to 900milliseconds. In a quiet state, the cardiac cycle may be about 800milliseconds. For example, the length of the time period may be equal to800 milliseconds. By limiting the length of the physiological data toless than or equal to the cardiac cycle, at most one position of featuredata (e.g., the R wave) may be identified from the same physiologicaldata, which may simplify the training process, improve the accuracyand/or training efficiency of the machine learning model, and/oravoiding an omission of the feature data. In some embodiments, thelength of the time period of the physiological data may exceed a cardiaccycle. For example, the length of the time period may be equal to 1000milliseconds.

In some embodiments, each of the plurality of training samples mayinclude annotated physiological data corresponding to the physiologicaldata. In some embodiments, the physiological data corresponding to eachof the training samples may be annotated by identifying the feature data(e.g., the R wave) from the physiological data. The identification ofthe feature data may include locating and/or marking the feature datafrom the physiological data. The physiological data may be used as aninput in a training process of a machine learning model. The annotatedphysiological data with marked feature data (i.e., annotatedphysiological data) may be used as a reference output corresponding tothe physiological data in the training process of the machine learningmodel. The feature data may be identified manually or automatically. Forexample, the processing device 120 may identify the feature data usingan R wave detection algorithm, such as a threshold segment algorithm.The feature data may be marked using a bounding box in the physiologicaldata. The bounding box may enclose feature data (e.g., the R wave) inthe physiological data. The bounding box may have any shape and/or size.For example, the bounding box may have the shape of a square, arectangle, a triangle, a polygon, a circle, an ellipse, an irregularshape, or the like.

In some embodiments, each of the plurality of training samples mayinclude a label corresponding to the physiological data. Thephysiological data may be used as an input in the training process of amachine learning model. The label corresponding to the physiologicaldata may be used as a reference output corresponding to thephysiological data in the training process of the machine learningmodel. The physiological data corresponding to each of the plurality oftraining samples may be annotated by the label (i.e., training label)indicating whether the physiological data satisfies a trigger condition(i.e., the trigger condition is satisfied).

The physiological data satisfying the trigger condition means that thecurrent physiological motion of the subject is significant, such asduring a tension phase (e.g., time period t1 as shown in FIG. 10 ), orthe physiological motion of the subject transfer from a rest phase tothe tension phase. The physiological data not satisfying the triggercondition means that the current physiological motion of the subject ismild, such as during the rest phase. For instance, physiological datanot satisfying the trigger condition refers to that the physiologicaldata lack the feature data (e.g., an R wave) located at a specificsection of a time period of the physiological data; physiological datasatisfying the trigger condition refers to that physiological dataincludes the feature data located at a specific section of a time periodof the physiological data. The specific section of the time period ofthe physiological data may also be referred to as a trigger window.

If the physiological data satisfies the trigger condition, thephysiological data may be a positive training sample. If thephysiological data is unsatisfied the trigger condition, thephysiological data may be a negative training sample. The label of thephysiological data may include a positive label or a negative label. Thephysiological data may be tagged with a negative label if thephysiological data is a negative training sample. The physiological datamay be tagged with a positive label if the physiological data is apositive training sample. The physiological data may be tagged with abinary label (e.g., 0 or 1, positive or negative, etc.). For example, anegative training sample may be tagged with a negative label (e.g.,“0”), while a positive training sample may be tagged with a positivelabel (e.g., “1”).

The specific section of the time period may be defined from a time pointin the time period to an ending time of the time period. In someembodiments, a length of the specific section of the time period may beless than a length of an acquisition window during which an imagingdevice acquires image data. The acquisition window may be located afterthe feature data with a trigger delay. The length of the acquisitionwindow of the imaging device may be set by an operator or according to adefault setting of the medical system 100. For example, the length of anacquisition window of an imaging device for acquiring image data may be20 milliseconds, 30 milliseconds, 50 milliseconds, etc. The imagingdevice may include an MRI device, a PET device, a SPECT device, a PET-CTdevice, etc., as described elsewhere in the present disclosure (e.g.,FIG. 1 and the descriptions thereof). In some embodiments, the length ofthe specific section of the time period may be in a range between 10milliseconds and 50 milliseconds, or in a range between 10 and 20milliseconds, etc. For example, the length of the specific section ofthe time period may be 10 milliseconds, 15 milliseconds, 20milliseconds, 25 milliseconds, 30 milliseconds, 35 milliseconds, 40milliseconds, 45 milliseconds, 50 milliseconds, etc.

In some embodiments, the plurality of training samples may include aplurality of positive samples and a plurality of negative samples. Eachof the plurality of positive samples may include first physiologicaldata that includes the feature data located at in a specific section ofa time period of the first physiological data, for example, the firstphysiological data in FIG. 11 . More descriptions regarding the firstphysiological data may be found in FIG. 11 and the descriptions thereof.Each of the plurality of negative samples may include secondphysiological data that lacks the feature data located at the specificsection of a time period of the second physiological data, for example,a second physiological data e in FIG. 12 . More descriptions regardingthe second physiological data may be found in FIG. 12 and thedescriptions thereof. In some embodiments, in the plurality of trainingsamples, a count (or number) of the negative samples may exceed thecount (or number) of the positive sample, such as 2-3 times the count ofthe positive samples. The more negative samples than the positivesamples may improve accuracy and F1 score of the trained machinelearning model, thereby improving the accuracy of the output result(e.g., the feature data detection) of the trained machine learningmodel.

In some embodiments, the processing device 120 may perform apretreatment operation on each of at least a portion of the plurality oftraining samples. The pretreatment operation may be the same as ordifferent from the pretreatment operation on the physiological data inoperation 502. In some embodiments, the pretreatment operation mayinclude at least one of a normalization operation, a denoisingoperation, a smoothing operation, or an downsampling operation. Moredescriptions regarding the pretreatment operations may be found in FIG.5 and the descriptions thereof. The pretreatment operation may improvethe efficiency of training the machine learning model.

In some embodiments, each of the plurality of training samples mayinclude the physiological data and a gating weighting function (or agating curve) corresponding to the physiological data. The physiologicaldata may be used as input in the training process of a machine learningmodel. The gating weighting function corresponding to the physiologicaldata may be used as a reference output corresponding to thephysiological data in the training process of the machine learningmodel. The gating weighting function may be determined based on thefeature data identified from the physiological data. The gatingweighting function may include a plurality of weighting values each ofwhich corresponds a portion of the physiological data. The weightingvalues may be in a range from 0 to 1. For example, if the subject movesdramatically during the tension phase (e.g., QT interval after the Rwave as shown in FIG. 10 ), weighting values corresponding to thetension phase may be relatively small, such as approximate to or equalto 0; if the subject moves gently during the resting phase (e.g., PRinterval before the R wave as shown in FIG. 10 ), the weighting valuescorresponding to the resting phase may be relatively large, such asapproximate to or equal to 1.

In 604, the processing device 120 (e.g., the training module 460) mayinitialize parameter values of a machine learning model.

In some embodiments, the machine learning model to be trained mayinclude a deep learning model (e.g., a convolutional neural network(CNN) model, a deep belief nets (DBN) machine learning model, a stackedauto-encoder network, etc.), a recurrent neural network (RNN) model, along short term memory (LSTM) network model, a fully convolutionalneural network (FCN) model, a generative adversarial network (GAN)model, a back propagation (BP) machine learning model, a radial basisfunction (RBF) machine learning model, an Elman machine learning model,or the like, or any combination thereof. In some embodiments, themachine learning model may include a multi-layer structure. For example,the machine learning model may include an input layer, an output layer,and one or more hidden layers between the input layer and the outputlayer. In some embodiments, the hidden layers may include one or moreconvolution layers, one or more rectified-linear unit layers (ReLUlayers), one or more pooling layers, one or more fully connected layers,or the like, or any combination thereof. As used herein, a layer of amodel may refer to an algorithm or a function for processing input dataof the layer. Different layers may perform different kinds of processingon their respective input. A successive layer may use output data from aprevious layer of the successive layer as input data. In someembodiments, the convolutional layer may include a plurality of kernels,which may be used to extract a feature. In some embodiments, each kernelof the plurality of kernels may filter a portion (i.e., a region). Thepooling layer may take an output of the convolutional layer as an input.The pooling layer may include a plurality of pooling nodes, which may beused to sample the output of the convolutional layer, so as to reducethe computational load of data processing and accelerate the speed ofdata processing speed. In some embodiments, the size of the matrixrepresenting the inputted data may be reduced in the pooling layer. Thefully connected layer may include a plurality of neurons. The neuronsmay be connected to the pooling nodes in the pooling layer. In the fullyconnected layer, a plurality of vectors corresponding to the pluralityof pooling nodes may be determined based on a training sample, and aplurality of weighting coefficients may be assigned to the plurality ofvectors. The output layer may determine an output based on the vectorsand the weighting coefficients obtained from the fully connected layer.

In some embodiments, each of the layers may include one or more nodes.In some embodiments, each node may be connected to one or more nodes ina previous layer. The number of nodes in each layer may be the same ordifferent. In some embodiments, each node may correspond to anactivation function. As used herein, an activation function of a nodemay define an output of the node given input or a set of inputs. In someembodiments, each connection between two of the plurality of nodes inthe initial machine learning model may transmit a signal from one nodeto another node. In some embodiments, each connection may correspond toa weight. As used herein, a weight corresponding to a connection may beused to increase or decrease the strength or impact of the signal at theconnection.

The machine learning model may include a plurality of parameters, suchas architecture parameters, learning parameters, etc. Exemplaryarchitecture parameters of the machine learning model may include thesize of a kernel of a layer, the total count (or number) of layers, thecount (or number) of nodes in each layer, a learning rate, a batch size,an epoch, etc. Exemplary learning parameters may include a connectedweight between two connected nodes, a bias vector relating to a node,etc.). Before the training, the machine learning model may have one ormore initial parameter values. In the training of the machine learningmodel, learning parameters of the machine learning model may be updated.Before the updating process, values of the learning parameters of themachine learning model may be initialized. For example, the connectedweights and/or the bias vector of nodes of the initial machine learningmodel may be initialized by assigning random values in a range, e.g.,the range from −1 to 1. As another example, all the connected weights ofthe initial machine learning model may be assigned the same value in therange from −1 to 1, for example, 0. As still an example, the bias vectorof nodes in the initial machine learning model may be initialized byassigning random values in a range from 0 to 1. In some embodiments, theparameters of the initial machine learning model may be initializedbased on a Gaussian random algorithm, a Xavier algorithm, etc. Moredescriptions regarding the convolutional neural network (CNN) model maybe found in FIG. 9 and the descriptions thereof.

In 606, the processing device 120 (e.g., the training module 460) maygenerate the trained machine learning model by iteratively updating,based on the plurality of training samples, the parameter values of themachine learning model.

In the training of the machine learning model, the processing device 120may iteratively update the parameter value(s) of the machine learningmodel based on the plurality of training samples. The updating of thelearning parameters of the machine learning model may be also referredto as updating the machine learning model. For example, the processingdevice 120 may update the model parameter(s) of the machine learningmodel by performing one or more iterations until a termination conditionis satisfied, wherein at least one of the iteration(s) may include oneor more operations of process 700 as described in connection with FIG. 7.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, one or more operations may be omitted and/or one or moreadditional operations may be added. For example, operation 604 andoperation 606 may be combined into a single operation. As anotherexample, one or more other optional operations (e.g., a storingoperation) may be added elsewhere in the process 600. In the storingoperation, the processing device 120 may store information and/or data(e.g., parameter values, etc.) associated with the training of themachine learning model in a storage device (e.g., the storage device130) disclosed elsewhere in the present disclosure.

FIG. 7 is a flowchart illustrating an exemplary training process of atrained machine learning model according to some embodiments of thepresent disclosure. In some embodiments, process 700 may be implementedas a set of instructions (e.g., an application) stored in the storagedevice 130, storage 220, or storage 390. The processing device 120, theprocessor 210 and/or the CPU 340 may execute the set of instructions,and when executing the instructions, the processing device 120, theprocessor 210 and/or the CPU 340 may be configured to perform theprocess 700. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 700may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of process 700 illustrated in FIG. 7 anddescribed below is not intended to be limiting. In some embodiments, thetraining process of the trained machine learning model as described inconnection with operation 606 in FIG. 6 may be performed according tothe process 700. For illustration purposes, a current iteration of theiteration(s) is described in the following description. The currentiteration may include one or more operations of the process 700.

In 702, the processing device 120 (e.g., the obtaining module 450) mayinput at least one training sample of the plurality of training samplesinto a machine learning model. Each of the plurality of training samplesmay include an input (i.e., physiological data) and a reference output(e.g., annotated physiological data) in the training process of themachine learning model. For example, the reference output may includethe annotated physiological data with identified feature data. Asanother example, the reference output may include a label (a positivelabel “1” or a negative label “0”) indicating whether the physiologicaldata satisfies a trigger condition, i.e., whether the physiological dataincludes feature data that is located at a specific section of a timeperiod of the physiological data. As still another example, thereference output may include a gating curve corresponding to thephysiological data. More descriptions regarding the plurality oftraining samples and the machine learning model may be found in FIG. 6and the descriptions thereof. For example, parameter values of themachine learning model may be initialized.

In 704, the processing device 120 (e.g., the training module 460) maygenerate, based on the at least one training sample, an estimated outputusing the machine learning model. The machine learning model maygenerate the estimated output by processing the inputted physiologicaldata based on the reference output.

In some embodiments, if the reference output indicates that thephysiological data is a positive sample or a negative sample, theestimated output may indicate that the physiological data is a positivesample or a negative sample. In other words, the estimated output mayindicate whether the physiological data satisfies the trigger condition.For example, the estimated output may include a probability that thephysiological data satisfies the trigger condition or the physiologicaldata includes feature data that is located at the specific section ofthe time period of the physiological data.

In some embodiments, if the reference output includes the annotatedphysiological data with identified feature data, the estimated outputmay include the physiological data with estimated feature data. Forexample, the estimated output may include at least a portion of thephysiological data with estimated position information associated withat least one of a peak of an R wave, a rising edge of the R wave, or afalling edge of the R wave in the physiological data (e.g., ECG data).

In some embodiments, if the reference output includes a gating weightingfunction determined based on the feature data identified from thephysiological data, the estimated output may include an estimated gatingweighting function. More descriptions regarding the feature data may befound in FIG. 5 and the descriptions thereof.

In 706, the processing device 120 (e.g., the training module 460) mayobtain an assessment result by assessing a difference between theestimated output and a reference output corresponding to the at leastone training sample.

In some embodiments, the assessment result may be a value of a costfunction relating to the difference between the estimated output and thereference output. For example, the processing device 120 (e.g., thetraining module 460) may determine the value of the cost functionrelating to the difference between the estimated output and thereference output. As used herein, the cost function (or loss function)may refer to a function that measures a difference between the estimatedoutput of the machine learning model and the reference output (i.e., anactual output), wherein the difference may indicate the accuracy of themachine learning model. In some embodiments, the cost function mayinclude a Softmax cross entropy loss function or a square error lossfunction.

In 708, the processing device 120 (e.g., the training module 460) maydetermine whether a termination condition is satisfied. The terminationcondition may provide an indication of whether the machine learningmodel is sufficiently trained. The termination condition may relate to acost function or an iteration count of the training process. Forexample, the processing device 120 may determine a loss function of themachine learning model and determine a value of the cost function basedon the difference between the estimated output and the actual output ordesired output (i.e., reference output). Further, the processing device120 may determine the termination condition is satisfied if the value ofthe loss function is less than a threshold. The threshold may be defaultsettings of the medical system 100 or may be adjustable under differentsituations. As another example, the termination condition may besatisfied if the value of the cost function converges. The convergencemay be deemed to have occurred if the variation of the values of thecost function in two or more consecutive iterations is smaller than athreshold (e.g., a constant). As still another example, the processingdevice 120 may determine the termination condition is satisfied if aspecified number (or count) of iterations are performed in the trainingprocess.

In response to a determination that the termination condition issatisfied, the processing device 120 may proceed to operation 710. In710, the processing device 120 may designate the machine learning modelwith the parameter values updated in the last iteration as the trainedmachine learning model (e.g., a trained machine learning model). On theother hand, in response to a determination that the terminationcondition is not satisfied, the processing device 120 may proceed tooperation 712. In 712, the processing device 120 may update at leastsome of the parameter values of the machine learning model based on theassessment result. For example, the processing device 120 may update thevalue(s) of the learning parameter(s) of the machine learning modelbased on the value of the loss function according to, for example, abackpropagation algorithm.

After 712, the processing device 120 may proceed to operation 702 toperform the next iteration until the termination condition is satisfied.In the next iteration, the processing device 120 may obtain multiplegroups of training samples in another batch. The size of the batch mayrefer to a group count or number of the multiple groups of trainingsamples. After the termination condition is satisfied in a certainiteration, the machine learning model in the certain iteration havingthe updated value(s) of the learning parameter(s) may be designated asthe trained machine learning model (e.g., the trained machine learningmodel).

In some embodiments, after 710, the processing device 120 may test thetrained machine learning model by inputting at least one of a pluralityof testing samples. The testing sample may be a plurality ofphysiological data. In some embodiments, the testing sample may beprocessed in the same manner as the training samples. For example, thetesting sample may include a plurality of positive samples and aplurality of negative samples. As another example, in the plurality oftesting samples, a count of the negative samples may be 2-3 times acount of the positive samples. As still another example, the pluralityof testing samples may be preprocessed as described in operations 502and 602. However, the physiological data of the testing sample may bedifferent with the physiological data of the training sample. Hence,test results may be ensured accuracy, which may avoid reducingapplication of the machine learning model.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional operations (e.g., a storing operation) may be addedelsewhere in the process 700. In the storing operation, the processingdevice 120 may store information and/or data (e.g., a training sample,the trained machine learning model, etc.) associated with the medicalsystem 100 in a storage device (e.g., the storage device 130) disclosedelsewhere in the present disclosure.

FIG. 8 is a flowchart illustrating an exemplary process for image dataacquisition according to some embodiments of the present disclosure. Insome embodiments, process 800 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210 and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the processor 210and/or the CPU 340 may be configured to perform the process 800. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 800 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order of theoperations of process 800 illustrated in FIG. 8 and described below isnot intended to be limiting. In some embodiments, one or more operationsof the process 800 may be performed to achieve at least part ofoperation 506 as described in connection with FIG. 5 .

In 802, the processing device 120 (e.g., the control module 430) mayacquire physiological data. More descriptions for the physiological datamay be found elsewhere in the present disclosure (e.g., FIG. 5 and thedescriptions thereof).

In some embodiments, the physiological data may be obtained byextracting data acquired over time by a monitoring device using asliding window with width k and step length λ. The physiological datamay also be referred to as an extracted fragment (e.g., fragment 1,fragment 2, fragment 3 as shown in FIGS. 13A-13C). The width of thesliding window may be equal to the length of the physiological data(i.e., each extracted fragment). In some embodiments, the step length λmay be a sampling period of the monitoring device. In some embodiments,the step length λ may be less than or equal to a length of the timeperiod of the feature data (e.g., R wave) of the physiological data. Forexample, as shown in FIG. 13B, the step length λ1 may be less than thelength of the time period m of the R wave in the ECG data. As shown inFIG. 13C, the step length λ2 may be equal to the length of the timeperiod m of the R wave in the ECG data. In some embodiments, the steplength λ may exceed the length of the time period of the feature data(e.g., R wave) of the physiological data. For example, the step length λmay be equal to 10 milliseconds, 20 milliseconds, etc. Two adjacentextracted fragments may have same physiological data when the steplength λ is less than a length of the time period of each extractedfragment.

In 804, the processing device 120 (e.g., the control module 430) maydetermine whether a trigger condition is satisfied based on thephysiological data (e.g., fragment 1 shown in FIG. 13 ). In response toa determination that the trigger condition is satisfied, the process 800may proceed to perform operation 806. In response to a determinationthat the trigger condition is unsatisfied, the process 800 may return toperform operation 802. The processing device 120 may obtain anotherphysiological data (e.g., fragment 2 shown in FIG. 13 ).

In some embodiments, the processing device 120 may identify feature datafrom the physiological data using a trained machine learning model asdescribed elsewhere in the present disclosure. The processing device 120may determine whether the trigger condition is satisfied based on thefeature data. For example, the processing device 120 may input thephysiological data into the trained machine learning model and thetrained machine learning model may output the physiological data withthe identified feature data or without the feature data. The processingdevice 120 may determine whether the feature data is located at aspecific section of the time period of the physiological data. Inresponse to a determination that the feature data is located at thespecific section of the time period of the physiological data (e.g.,fragment 2 shown in FIG. 13 ), the processing device 120 may determinethat the trigger condition is satisfied. In response to a determinationthat the physiological data (e.g., fragment 1 shown in FIG. 13 ) lacksthe feature data that is located at the specific section of the timeperiod of the physiological data, the processing device 120 maydetermine that the trigger condition is not satisfied.

The specific section of the time period of the physiological data may bedefined from a time point in the time period to an ending time of thetime period of the physiological data. In some embodiments, a length ofthe specific section of the time period may be less than a length of anacquisition window during which an imaging device acquires image data.For example, the length of the specific section of the time period maybe 20 milliseconds, 15 milliseconds, 10 milliseconds, etc. In someembodiments, the length of the specific section of the time period maybe equal to or exceed the size length of the sliding window. As afurther example, if the feature data is present at the end of 10milliseconds of the time period of the physiological data, theprocessing device 120 may determine that the trigger condition issatisfied. The feature data may include position information associatedwith a peak of an R wave, a rising edge of the R wave, and a fallingedge of the R wave in the ECG data, etc.

In some embodiments, the processing device 120 may determine whether thetrigger condition is satisfied using the trained machine learning modelas described elsewhere in the present disclosure. For example, theprocessing device 120 may input the physiological data into the trainedmachine learning model. The trained machine learning model may output aresult indicating whether the trigger condition is satisfied.

In 806, the processing device 120 (e.g., the control module 430) maygenerate a trigger pulse signal based on the feature data, in responseto determining that the trigger condition is satisfied. The triggerpulse signal may be configured to cause the imaging device to scan thesubject. In some embodiments, the trigger pulse signal may include atrigger delay for acquiring the image data from a reference time point.The reference time point may be when the feature data is detected, whenthe preceding pulse is applied, when the trigger pulse signal generates,etc. For example, the trigger delay may be 20 milliseconds.

In 808, the processing device 120 (e.g., the control module 430) maycause the imaging device to scan the subject based at least in part onthe trigger pulse signal. The imaging device may be configured toacquire image data relating to at least one part of a subject. Moredescriptions regarding the imaging device may be found in FIG. 1 and thedescriptions thereof. In some embodiments, if one single trigger pulsesignal is generated, the processing device 120 (e.g., the control module430) may cause the imaging device to scan the subject based on the onesingle trigger pulse signal. For example, the processing device 120(e.g., the control module 430) may cause an MRI device to scan thesubject after a trigger delay from the time the one single trigger pulsegenerates.

In some embodiments, the processing device 120 (e.g., the control module430) may determine whether a specific count (or number) of multipletrigger pulse signals are generated. In response to determining that thespecific count (or number) of consecutive trigger pulse signals aregenerated, the processing device 120 (e.g., the control module 430) maycause the imaging device (e.g., an MRI device) to scan the subject aftera trigger delay from the time the last trigger pulse signal generates.For example, along the data acquisition of the monitoring device, thesliding window may slide to obtain multiple extracted fragments with thestep length. Each extracted fragment acquired by the sliding window maybe inputted into the trained machine learning model to obtain an outputresult and the output result may be arranged in chronological order.Further, if the trigger condition is satisfied, the trained machinelearning model may output a true value. If the trigger condition isunsatisfied, the trained machine learning model may output a falsevalue. The output values (i.e., true values and/or false values) of thetrained machine learning model may eventually cause trigger pulsesignals generation to form a pulse signal waveform with a samplingperiod of the step length of the sliding window. Each trigger pulsesignal in the pulse signal waveform may represent at least a portion ofthe feature data. In some embodiments, the pulse signal waveform mayinclude information about scanning parameters (e.g., scanning time,frequency, or the like, or any combination thereof).

In some embodiments, the step length (e.g., 2 milliseconds) of thesliding window may be less than the length of the time period (e.g., 10milliseconds) of the feature data (e.g., an R wave). As shown in FIG.13B, the step length λ1 is less than the length of the time period m ofthe R wave in the ECG data. The trained machine learning models mayoutput several consecutive truth values until an extracted fragmentinputted into the trained machine learning model includes the entirefeature data (e.g., an R wave). The processing device 120 may cause theimaging device to acquire the image data until determining that thespecific count (or number) of consecutive truth values are generated.

For example, when the step length of the sliding window is 1 millisecondand the length of the specific section of the time period of thephysiological data is 10 milliseconds that is equal to the length of thetime period of the feature data, at least 10 extracted fragments mayinclude at least a portion of the feature data (e.g., an R wave). Eachof the 10 extracted fragments may be inputted into the trained machinelearning model. The trained machine learning model may continuouslydetect at least a portion of the feature data for 10 times each of whichcorresponds to one of the 10 extracted fragments. In other words, thetrained machine learning models may output 10 consecutive truth values.The processing device 120 may cause the imaging device to scan thesubject until the 10 consecutive truth values are outputted by thetrained machine learning model. Accordingly, the imaging device (e.g.,an MRI device) may be caused to acquire the image data until the featuredata is detected continuously for multiple (e.g., 3) times, which maygreatly reduce the probability of detection error of the feature datausing the trained machine learning model.

In addition, since only one scan is performed during a cardiac cycle(about 800 milliseconds), in response to a determination that thetrigger condition is satisfied, the processing device 120 may furtherdetermine whether a difference between a time when the last extractedfragment (e.g., fragment 1 as shown in FIG. 13A) is acquired and acurrent time when a current extracted fragment (e.g., fragment 2 asshown in FIG. 13B fragment 3 as shown in FIG. 13C) is acquired exceeds athreshold (e.g., 100 milliseconds). In response to determining that thedifference between the time when the last extracted fragment is acquiredand the current time when the current extracted fragment acquiredexceeds the threshold (e.g., 100 milliseconds), the processing device120 may trigger the imaging device to acquire the image data, which mayavoid to repeatedly perform scans during a cardiac cycle. The thresholdmay be greater than the length of the acquisition window but less thanthe cardiac cycle, such as 100 milliseconds.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional operations (e.g., a storing operation) may be addedelsewhere in the process 800. In the storing operation, the processingdevice 120 may store information and/or data (e.g., a training sample,the trained machine learning model, etc.) associated with the medicalsystem 100 in a storage device (e.g., the storage device 130) disclosedelsewhere in the present disclosure.

FIG. 9 is a schematic diagram illustrating an exemplary CNN model 900according to some embodiments of the present disclosure. In someembodiments, the CNN model 900 may be a component of a trained machinelearning model as described elsewhere in this disclosure (e.g., FIGS.5-7 and the relevant descriptions).

The machine learning model in the embodiment may include any artificialneural network that may realize a deep learning algorithm. Theartificial neural networks may be proven and successfully implemented indata prediction related applications, such as data trend prediction,speech recognition, or the like. In the artificial Neural Networks,Convolutional Neural Networks (CNN for short) may be a kind ofFeedforward Neural Networks with convolution computation and depthstructure. The CNN model may be one of the representative algorithms ofdeep learning algorithms. The CNN models may have the ability ofrepresentational learning and translate input information withoutchanging the classification according to the hierarchical structure.

The artificial neural network may have the characteristics of memory,parameter sharing, and Turing completeness. Hence, the artificial neuralnetwork may have advantages in learning the nonlinear characteristics ofsequence data. Physiological data (e.g., electrocardiogram (ECG) data,respiration data, or the like) may have strong regularity and coherence,which is a typical sequence data. Therefore, in the embodiment, theartificial neural network may be used to learn the physiological dataand determine feature data.

In the embodiment, the architecture of a neural network may beimplemented by tensorflow. As shown in FIG. 9 , the CNN model 900 mayinclude an input layer 902, a plurality of hidden layers 904, and anoutput layer 906. The hidden layers 904 may include one or moreconvolutional layers, one or more rectified linear unit (ReLU) layers,one or more pooling layers, one or more fully connected layer, or thelike, or any combination thereof. For illustration purposes, exemplaryhidden layers 904, including a convolutional layer 904-1, a poolinglayer 904-2, and a fully connected layer 904-N, are provided in FIG. 9 .

The input layer 902 may be used for data input. For example, the inputdata may be physiological data in the process of application of the CNNmodel 900. As another example, the input data may be a training samplein the process of training. Since the physiological data is waveformdata without color information, the input of the CNN model 900 may betwo-dimensional data.

The convolutional layer 904-1 may include a plurality of kernels (e.g.,A, B, C, and D), which may be used to extract a feature of thephysiological data. The physiological data may be inputted into theconvolutional layer 904-1 in the form of an image. In some embodiments,each kernel of the plurality of kernels may filter a portion of theimage (e.g., an ECG) to generate a specific image feature correspondingto the portion. The specific image feature may be determined based onthe kernels. Exemplary image features may include a low-level feature(e.g., an edge feature, a textural feature), a high-level feature, or acomplicated feature. As used herein, the low-level feature (e.g., anedge feature, a textural feature) may be extracted by a low convolutionlayer, while the more layered network may iteratively extract morecomplex features (e.g., the high-level feature, the complicated feature)from low-level features.

In some embodiments, a normalization layer may be present (not shown) inthe CNN model. The normalization layer may be used to force an inputdistribution which gradually maps to a nonlinear function and then to alimit saturation region of a value interval to return to a standardnormal distribution with a mean value of 0 and a variance of 1.Therefore, the input value of the nonlinear transformation function mayfall into a region that is sensitive to the input, so as to avoid agradient vanishing problem.

The pooling layer 904-2 may take an output of the convolutional layer904-1 as an input. The pooling layer 904-2 may include a plurality ofpooling nodes (e.g., E, F, G, and H), which may be used to sample theoutput of the convolutional layer 904-1, so as to reduce thecomputational load of data processing and accelerate the speed of dataprocessing speed. In some embodiments, the size of the matrixrepresenting the physiological data may be reduced in the pooling layer904-2. The pooling layer 904-2 may improve the model classification andidentification, provide nonlinearity, reduce the count of modelparameters, and reduce the over-fitting problem.

The fully connected layer 904-N may include a plurality of neurons(e.g., O, P, M, and N). The neurons may be connected to the poolingnodes in the pooling layer 904-2. In the fully connected layer 904-N, aplurality of vectors corresponding to the plurality of pooling nodes maybe determined based on one or more features of the physiological data,and a plurality of weighting coefficients may be assigned to theplurality of vectors. Therefore, the fully connected layer 904-N may beconfigured to refit the tail of the CNN model to reduce the loss offeature information.

In some embodiments, a loss layer may be present (not shown) in the CNNmodel. The loss layer may include two inputs, one of which may be anestimated output of the CNN model and another may be a reference output(i.e., an actual output). The loss layer may perform a series ofoperations on these two inputs to obtain a cost function of the currentnetwork. The purpose of deep learning may be to find a weight thatminimizes the cost function in a weight space. The cost function may beobtained in the forward propagation calculation, and also a beginningpoint of the backpropagation. The cost function may be basicallycomposed of the estimated output and the reference output. The correctcost function may make the estimated output approximate to the referenceoutput. In some embodiments, the cost function may include a Softmaxcross entropy loss function or a square error loss function.

The output layer 906 may determine an output based on the vectors andthe weighting coefficients obtained from the fully connected layer904-N. In some embodiments, an output of the output layer 906 mayinclude a probability map, a classification map, and/or a regressionmap. For example, the output of the output layer 906 may include aprobability that a trigger condition is satisfied. As another example,the output of the output layer 906 may include a gating weightingfunction. More descriptions regarding the determination may be found inFIGS. 5, 7, and 8 , and the descriptions thereof.

In some embodiments, adopting the CNN model for deep learning, thephysiological data may balance the representation ability of the CNNmodel and the computational cost of training network. Furthermore, abatch normalization layer may be preferred in the embodiment. Comparedwith a local response normalization layer, the batch normalization layermay improve the gradient across CNN model and allow for a greater rateof learning, thereby increasing the speed of training.

In some embodiments, the CNN model may be implemented on one or moreprocessing devices (e.g., the processing device 120). In someembodiments, a plurality of processing devices may execute a parallelprocessing operation in some layers of the CNN model 900 by, forexample, assigning two or more processing devices for an operation ofdifferent nodes (e.g., a kernel, a pooling node, a neuron) in the CNNmodel 900. For example, a first GPU may execute the operationcorresponding to the kernel A and kernel B, and a second kernel mayexecute the operation corresponding to the kernel C and kernel D.Similarly, a plurality of GPUs may also execute the operation of othernodes (e.g., a kernel, a pooling node, a neuron) in the CNN model 900.In addition, in some embodiments, a storage device (e.g., the storagedevice 130, the storage 220 of the computing device 200) may be providedfor storing data related to the CNN model 900, such as activations andlearned weights for each node.

It should be noted that the example in FIG. 9 is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theCNN model 900 may include one or more additional components.Additionally or alternatively, one or more components of the CNN model900 described above may be omitted. As another example, the CNN model900 may include any number of layers and nodes.

FIG. 10 is a schematic diagram illustrating an exemplary ECG accordingto some embodiments of the present disclosure. The ECG may include aplurality of cycles indicating normal sinus rhythm. Generally, eachcycle may include a P wave, a QRS complex, a T wave, as shown in FIG. 10. The QRS complex may include a Q wave, an R wave, and an S wave. Asshown in FIG. 10 , for each cycle of the ECG, a PR interval refers to aduration that extends from the beginning of the P wave to the beginningof the QRS complex. A PR segment refers to a duration that extends fromthe end of the P wave to the beginning of the QRS complex. A QT intervalrefers to a duration that extends from the beginning of the QRS complexto the end of the T wave. An ST segment refers to a duration thatextends from the end of the QRS complex to the beginning of the T wave.As illustrated in FIG. 10 , for the ECG, the R wave of the QRS complexmay be the most significant wave among the components of one cycle ofthe ECG (e.g., the P wave, the QRS complex, the T wave). In someembodiments, the ECG may be analyzed by dividing one or more cardiaccycles. For example, an R wave may be designated as a beginning and/oran end of the cardiac cycle, which records the performance of the humanheart from the beginning of one heartbeat to the end of the oneheartbeat (or the beginning of a next heartbeat). A cardiac cycle mayrefer to a duration between two consecutive R waves. One cardiac cyclemay be denoted as an R-R interval, or an R-R for short. A cardiac cyclemay include a tension phase corresponding to a time period t1 from thepeak of R wave to the end of T wave, and a resting (or relaxation) phasecorresponding to a time period t2 from the end of T wave to the peak ofR wave generated in the next cycle. The acquisition of image data (e.g.,MR data) using a gating trigger technique as described in FIG. 8 may beperformed during a time period (e.g., the middle to late stage) in theresting (or relaxation) phase (i.e., the time period t2). Accordingly, Rwaves may be to determine the tension phase and/or the resting (orrelaxation) phase for imaging of movement subject (e.g., the heart). Thetrained machine learning model determined using a plurality of trainingsamples may improve the accuracy and efficiency of R wave detection.

FIG. 11 is a schematic diagram illustrating first physiological datasatisfying a trigger condition according to some embodiments of thepresent disclosure. FIG. 12 is a schematic diagram illustrating secondphysiological data not satisfying the trigger condition according tosome embodiments of the present disclosure.

As shown in FIG. 11 and FIG. 12 , the lengths of the time periods of thefirst physiological data and the second physiological data are the same,800 milliseconds, an average length of cardiac cycles. The firstphysiological data includes feature data (e.g., an R wave) located at aspecific section (e.g., at the end of 10 milliseconds) of the timeperiod of the first physiological data, i.e., the first physiologicaldata satisfies a trigger condition. The second physiological data lacksfeature data (e.g., the R wave) located at the specific section (e.g.,at the end of 10 milliseconds) of the time period of the secondphysiological data, i.e., the second physiological data does not satisfya trigger condition. The feature data may include position informationassociated with at least one of a peak of an R wave, a rising edge ofthe R wave, or a falling edge of the R wave in the ECG data. Thespecific section of the time period is at the end of 10 milliseconds ofthe time period of the first physiological data or the secondphysiological data.

In some embodiments, the first physiological data may be labeled as apositive training sample and the second physiological data fragment maybe labeled as a negative training sample of the trained machine learningmodel as described elsewhere in the present disclosure (e.g., FIG. 6 andthe descriptions thereof).

FIGS. 13A-13C are schematic diagrams illustrating an exemplary processfor physiological data acquisition according to some embodiments of thepresent disclosure.

As shown in FIGS. 13A-13C, the physiological data may include ECG data.The ECG data are acquired by a monitoring device over time, and multipleextracted fragments (e.g., fragment 1, fragment 2, fragment 3) of ECGdata may be acquired using a sliding window with width k and step lengthλ. Each of the extracted fragments may be inputted into the trainedmachine learning model as described elsewhere in the present disclosureto obtain an output result. For example, the output result may include apositive value if the extracted fragment includes R wave located at thespecific section (e.g., at the end of 10 milliseconds) of the timeperiod of the extracted fragment.

FIG. 13A shows a current extracted fragment 1 obtained at a currenttime. FIG. 13B shows a next extracted fragment 2 adjacent to the currentextracted fragment 1 acquired by the sliding window with step length λ1.As shown in FIG. 13B, the step length λ1 is less than the length of thetime period m of the R wave in the ECG data. The extracted fragment 2includes a portion of the R wave. FIG. 13C shows a next extractedfragment 3 immediately following the current extracted fragment 1acquired by the sliding window with step length λ2. As shown in FIG.13C, the step length λ2 is equal to the length of the time period m ofthe R wave in the ECG data. The extracted fragment 3 includes anintegrated R wave.

The ECG data acquired before time point t or t+m has been acquired bythe monitoring device, and the ECG data acquired after time point t ort+m has not been acquired by the monitoring device. In FIGS. 13A-13Conly in order to describe the working process of the sliding window, theECG data that has not been acquired are shown.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer-readableprogram code embodied thereon.

A non-transitory computer-readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectromagnetic, optical, or the like, or any suitable combinationthereof. A computer-readable signal medium may be any computer-readablemedium that is not a computer-readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer-readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran, Perl,COBOL, PHP, ABAP, dynamic programming languages such as Python, Ruby,and Groovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, e.g., an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-20. (canceled)
 21. A system, comprising: at least one storage device storing executable instructions, and at least one processor in communication with the at least one storage device, when executing the executable instructions, causing the system to perform operations including: acquiring physiological data of a subject; obtaining a trained machine learning model; determining, based on the physiological data, an output result of the trained machine learning model, the output result including a gating weighting function defined by a plurality of weighting values corresponding to the physiological data; acquiring, based on the output result, image data of the subject.
 22. The system of claim 21, wherein each of the plurality of weighting values corresponds to a portion of the physiological data acquired at a time or period.
 23. The system of claim 21, wherein the acquiring, based on the output result, the image data of the subject includes: acquiring original image data of the subject by an imaging device synchronously with the acquisition of the physiological data by a monitoring device; and determining, based on the gating weighting function and the original image data, the image data.
 24. The system of claim 23, wherein the determining, based on the gating weighting function and the original image data, the image data includes: extracting the image data from the original image data based on the gating weighting function.
 25. The system of claim 24, wherein the extracting the image data from the original image data based on the gating weighting function includes: extracting image data from the original image data by multiplying the gating weighting function with the original image data.
 26. The system of claim 25, wherein the image data includes a portion of the original image data acquired by the imaging device at a time when the weighting values are non-zero.
 27. The system of claim 21, wherein the physiological data corresponds to a motion of the subject over time.
 28. The system of claim 21, wherein the physiological data includes at least one of electrocardiogram (ECG) data or respiration data.
 29. The system of claim 21, wherein the physiological data is acquired by a monitoring device based on at least one of: an echo signal generated by emitting, by the monitoring device, an electromagnetic wave to the subject, an ECG signal, a photoelectric signal generated by emitting, by the monitoring device, light beams to the subject, an oscillation signal generated when the monitoring device detects an oscillation caused by a motion of the subject, or a pressure signal generated when the monitoring device detects a pressure change caused by the motion of the subject.
 30. The system of claim 21, wherein the output result further includes: feature data represented in the physiological data; or a determination as to whether a trigger condition for triggering an imaging device to acquire original image data is satisfied.
 31. The system of claim 30, wherein the feature data includes position information associated with at least one of a peak of an R wave, a rising edge of the R wave, a falling edge of the R wave in the physiological data, a peak of a P wave, a rising edge of the P wave, or a falling edge of the P wave in the physiological data.
 32. The system of claim 30, wherein the acquiring, based on the output result, the image data of the subject includes: in response to determining that the trigger condition is satisfied, generating, based on the output result, a trigger pulse signal; and causing, based at least in part on the trigger pulse signal, the imaging device to scan the subject.
 33. The system of claim 32, wherein the trigger pulse signal includes a trigger delay for acquiring the image data from a reference time point.
 34. The system of claim 21, wherein the trained machine learning model is provided by a process including: obtaining a plurality of training samples; initializing parameter values of a machine learning model; and generating the trained machine learning model by iteratively updating, based on the plurality of training samples, the parameter values of the machine learning model.
 35. The system of claim 21, wherein the determining, based on the physiological data, the output result of the trained machine learning model includes: performing a pretreatment operation on the physiological data to obtain preprocessed physiological data; and generating the output result by inputting the preprocessed physiological data into the trained machine learning model.
 36. A system, comprising: at least one storage device storing executable instructions, and at least one processor in communication with the at least one storage device, when executing the executable instructions, causing the system to perform operations including: acquiring physiological data of a subject, the physiological data corresponding to a motion of the subject over time; obtaining a trained machine learning model; determining, based on the physiological data, an output result of the trained machine learning model, the output result including a determination as to whether a trigger condition for triggering an imaging device to acquire image data is satisfied, and the trigger condition indicates the motion of the subject is significant or the motion of the subject transfers from a rest phase to a tension phase; acquiring, based on the output result, the image data of the subject using the imaging device.
 37. The system of claim 36, wherein the acquiring, based on the output result, the image data of the subject using the imaging device includes: in response to determining that the trigger condition is satisfied, generating, based on the output result, a trigger pulse signal; and causing, based at least in part on the trigger pulse signal, the imaging device to scan the subject to obtain the image data of the subject.
 38. The system of claim 37, wherein the trigger pulse signal includes a trigger delay for acquiring the image data from a reference time point.
 39. The system of claim 38, wherein the causing, based at least in part on the trigger pulse signal, the imaging device to scan the subject to obtain the image data of the subject includes: causing the imaging device to scan the subject after the trigger delay from the time the trigger pulse generates.
 40. The system of claim 37, wherein the operations further include: determining that a plurality of consecutive trigger pulse signals are generated, and causing the imaging device to scan the subject after a trigger delay from the time a last trigger pulse signal generates. 